{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d244f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gym\n",
    "\n",
    "\n",
    "#定义环境\n",
    "class MyWrapper(gym.Wrapper):\n",
    "\n",
    "    def __init__(self):\n",
    "        env = gym.make('CliffWalking-v0', render_mode='rgb_array')\n",
    "        super().__init__(env)\n",
    "        self.env = env\n",
    "        self.step_n = 0\n",
    "\n",
    "    def reset(self):\n",
    "        state, _ = self.env.reset()\n",
    "        self.step_n = 0\n",
    "        return state\n",
    "\n",
    "    def step(self, action):\n",
    "        state, reward, done, _, info = self.env.step(action)\n",
    "        self.step_n += 1\n",
    "        if self.step_n >= 200:\n",
    "            done = True\n",
    "        return state, reward, done, info\n",
    "\n",
    "\n",
    "env = MyWrapper()\n",
    "\n",
    "env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b77c66d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#打印游戏\n",
    "def show():\n",
    "    plt.figure(figsize=(3, 3))\n",
    "    plt.imshow(env.render())\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f489de60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env.observation_space= Discrete(48)\n",
      "env.action_space= Discrete(4)\n",
      "state= 36\n",
      "action= 1\n",
      "next_state= 36\n",
      "reward= -100\n",
      "done= False\n",
      "info= {'prob': 1.0}\n"
     ]
    }
   ],
   "source": [
    "#认识游戏环境\n",
    "def test_env():\n",
    "    print('env.observation_space=', env.observation_space)\n",
    "    print('env.action_space=', env.action_space)\n",
    "\n",
    "    state = env.reset()\n",
    "    action = env.action_space.sample()\n",
    "    next_state, reward, done, info = env.step(action)\n",
    "\n",
    "    print('state=', state)\n",
    "    print('action=', action)\n",
    "    print('next_state=', next_state)\n",
    "    print('reward=', reward)\n",
    "    print('done=', done)\n",
    "    print('info=', info)\n",
    "\n",
    "\n",
    "test_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d5c572d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.dqn.dqn.DQN at 0x7f1c287eb3a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from stable_baselines3 import DQN\n",
    "from stable_baselines3.common.env_util import make_vec_env\n",
    "\n",
    "#初始化模型\n",
    "model = DQN(\n",
    "    policy='MlpPolicy',\n",
    "    env=make_vec_env(MyWrapper, n_envs=8),  #使用N个环境同时训练\n",
    "    learning_rate=1e-3,\n",
    "    buffer_size=10000,  #最多积累N步最新的数据,旧的删除\n",
    "    learning_starts=500,  #积累了N步的数据以后再开始训练\n",
    "    batch_size=64,  #每次采样N步\n",
    "    tau=0.8,  #软更新的比例,1就是硬更新\n",
    "    gamma=0.9,\n",
    "    train_freq=(1, 'step'),  #训练的频率\n",
    "    target_update_interval=1000,  #target网络更新的频率\n",
    "    policy_kwargs={},  #网络参数\n",
    "    verbose=0,\n",
    "    device='cpu')\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22afd2bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/envs/pt39/lib/python3.9/site-packages/stable_baselines3/common/evaluation.py:67: UserWarning: Evaluation environment is not wrapped with a ``Monitor`` wrapper. This may result in reporting modified episode lengths and rewards, if other wrappers happen to modify these. Consider wrapping environment first with ``Monitor`` wrapper.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-200.0, 0.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from stable_baselines3.common.evaluation import evaluate_policy\n",
    "\n",
    "evaluate_policy(model, env, n_eval_episodes=20, deterministic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75e0b593",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #训练N步\n",
    "# model.learn(20_0000, progress_bar=True)\n",
    "\n",
    "# #保存模型\n",
    "# model.save('save/2.DQN.Cliff Walking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdaaf12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-40.85, 56.709148291964325)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载模型\n",
    "model = DQN.load('save/2.DQN.Cliff Walking')\n",
    "\n",
    "evaluate_policy(model, env, n_eval_episodes=20, deterministic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15e5c726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-13 13 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\n"
     ]
    }
   ],
   "source": [
    "from IPython import display\n",
    "import random\n",
    "\n",
    "\n",
    "def test():\n",
    "    state = env.reset()\n",
    "    reward_sum = []\n",
    "    over = False\n",
    "    while not over:\n",
    "        action, _ = model.predict(state)\n",
    "        action = action.item()\n",
    "        state, reward, over, _ = env.step(action)\n",
    "        reward_sum.append(reward)\n",
    "\n",
    "        if len(reward_sum) % 1 == 0:\n",
    "            display.clear_output(wait=True)\n",
    "            show()\n",
    "\n",
    "    print(sum(reward_sum), len(reward_sum), reward_sum)\n",
    "\n",
    "\n",
    "test()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
